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Employing a multidisciplinary approach to gain an understanding of these complex conditions and incorporating disciplines such as engineering, biomechanics, material and computer science, and the basic medical sciences will lead to advances in our field.
Dr Hart is Chief Medical Officer of Innovation, USF Health Center for Advanced Medical Learning and Simulation (CAMLS), Division of Female Pelvic Medicine and Reconstructive Surgery, Department of Obstetrics and Gynecology, University of South Florida Morsani College of Medicine, Tampa. He reports receiving salary/honoraria and fees from Covidien and Boston Scientific, and performing contracted research for Cooper Surgical, Covidien, and Stryker.
Pelvic floor disorders, including pelvic organ prolapse (POP) and incontinence, are very common conditions that can significantly affect a patient’s quality of life, and are especially challenging disorders for clinicians to predict and treat.
POP affects up to half of women older than age 50 years and is one of the most common indications for gynecologic surgery. More than 226,000 POP surgeries are performed annually in the United States, with direct costs exceeding $1 billion per year.1-3 The lifetime risk of a woman undergoing a surgery for POP or incontinence by the age of 80 years is 11%, although some estimates place it as high as 20%.4-7
The number of surgeries for POP or incontinence, and the expense to our healthcare system as a result of pelvic floor disorders, will continue to rise because the elderly population is expected to increase to 50 million by 2019, while the number of women with POP is predicted to increase by 50% from 2010 to 2050.2,7,8 Thus, pelvic floor disorders have been called a hidden epidemic.9
The field of female pelvic medicine and reconstructive surgery (FPMRS) has made great strides during the past several decades. Due to the significant complexity of pelvic floor conditions, experts specializing in this field have developed an increasingly advanced diagnostic and surgical skillset, and are more frequently using evolving technologies such as imaging in the diagnosis and treatment of these conditions. Despite the many advances made in FPMRS, our understanding remains limited of the pathophysiology that underlies pelvic floor conditions, including POP and incontinence.
One of the most challenging aspects of the field is the high recurrence rate associated with currently available surgical procedures, especially for POP, with recurrence rates requiring reoperation reported as high as 29% in a community-based population.4,10-13 Studies have also shown that even our gold standard treatment for POP-sacrocolpopexy-may be less effective and associated with higher complication rates than previously thought.
In a 7-year follow-up of the CARE trial (Colpopexy And urinary Reduction Efforts), in which patients underwent abdominal sacrocolpopexy, the estimated probabilities of treatment failure (POP, stress urinary incontinence, urinary incontinence) in the urethropexy and no urethropexy groups were 0.27 and 0.22, respectively, for anatomic POP, and 0.29 and 0.24 for symptomatic POP. This study also showed that complications related to synthetic mesh continue to occur over time at a rate that is likely higher than previously thought, with a 10.5% probability of mesh erosion at 7 years postsurgery.11
A better understanding of the underlying causes of pelvic floor disorders is needed not only so that surgical treatment can be maximized to prevent recurrences, but also so that predictive models can be developed to determine which patients are at highest risk of developing these disorders so that preventative strategies can be implemented at an earlier stage of progression. Also, development of computational models capable of predicting surgical success and failure, through the use of patient-specific risk factors and diagnostic findings (examination and testing data), could completely change the surgical procedures we offer and ultimately improve outcomes.
The article in this issue by Dr Lennox Hoyte and colleagues on levator ani injury during childbirth provides an overview of the current state of the evidence regarding childbirth injury to the levator ani muscles, the associated risk to the development of pelvic floor disorders and of surgical failure, and methods to possibly reduce the risk of these injuries. Much of this knowledge has been made possible through advances in pelvic floor imaging, which could also one day provide the data needed to create more accurate predictive models.
NEXT: Pelvic floor imaging
Arguably one of the most promising areas of innovation within FPMRS is pelvic floor imaging, which could have a profound impact on how we evaluate, diagnose, treat, and even offer preventive therapies to our patients. Imaging techniques have the potential to provide objective confirmation of findings obtained on examination, such as levator ani injuries, or structural findings not obvious to the clinician focused on surface anatomy during a clinical examination. Ultimately, these imaging techniques could lead to enhanced clinical assessment and improved patient outcomes.14
Understanding the anatomic causes of childbirth injuries or other types of injuries to the pelvic floor and their relationship to the development of POP and incontinence, as outlined by Dr Hoyte, may one day enable the use of patient-specific pelvic floor imaging data, combined with other patient-specific data from examination, testing, and risk factors, to accurately predict treatment success (conservative or surgical intervention). Expanding research into pelvic floor anatomic imaging, combined with data analytic techniques that are revolutionizing many other industries, will no doubt advance our ability to develop more accurate predictive models.
At my institution, we have partnered with the college of engineering to explore the creation of these types of models by combining patient-specific risk factors, past history and findings from POP quantification examination with imaging measurements to create computational models in an effort to predict success after surgery for POP.
We have also created automated edge detention algorithms of the common reference lines used to evaluate POP on dynamic magnetic resonance imaging (MRI) studies, with the goal of automating interpretation of dynamic MRI studies. This will enable clinicians and researchers to create large repositories of data that can be used to create and/or further refine these types of models and potentially advance our understanding of pelvic floor disorders.15-18
It is also our hope that one day this information will enable screening for pelvic floor disorders so that preventive strategies can be implemented, possibly helping patients avoid the need for surgery.
We are entering an exciting time in which big data will drive population-based healthcare and improve the accuracy of diagnosis and treatment outcomes in many fields of medicine. Big data has revolutionized many other industries, and offers the same promise in FPMRS through collection of large amounts of patient-specific data.
Given the complexity of pelvic floor disorders and the growing acceptance of imaging in clinical practice, FPMRS is well positioned as a field to maximize utilization of these newer technologies and data analytic techniques by combining clinical data, testing data, and imaging data. Obviously cost is a significant issue in the use of any new technology, but if imaging can reduce rates of expensive repeat surgeries, it may become a cost-effective strategy for evaluating and treating pelvic floor disorders.
Employing a multidisciplinary approach to gain an understanding of these complex conditions and incorporating disciplines such as engineering, biomechanics, material and computer science, and the basic medical sciences will lead to advances in our field. The structural anatomic data obtained through imaging of patients with pelvic floor disorders may enhance our understanding of the underlying pathophysiology of this hidden epidemic, and help us reach the ultimate goal of improving the lives of our patients.
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